一种融合多维信息的移动社区发现方法

为解决传统社区发现算法难适用于大型复杂异质的移动网络的问题，利用移动网络使用详单数据（Usage Detail Record， UDR）和移动用户社交数据构建网络模型，提出一种融合多维信息的移动社区发现方法BNMF-NF。该方法综合考虑用户社交关系和时空行为，给出用户社交相似度、位置分布相似度和主题偏好相似度，利用加权网络融合方法融合多维相似关系构建用户相似网络，并运用有界非负矩阵分解技术实现社区结构的检测。在Foursquare和电信数据集上的实验结果表明，BNMF-NF方法能够有效发现移动网络中用户社区结构。

A Method for Mobile Community Detection Based on Multi-dimensional Informational Fusion

To address the issue that the traditional community detection algorithm is difficult to apply to large-scale complex heterogeneous mobile networks, a mobile network model is constructed using mobile network usage detail record （UDR） and users’ social relationship data, and a method for mobile community detection based on multi-dimensional informational fusion is proposed, called BNMF-NF. Firstly, the paper comprehensively considers the user’s social relationship and spatiotemporal behavior, and gives the user’s social similarity, spatiotemporal distribution similarity and topic preference similarity. Then, the weighted network fusion method is used to fuse multi-dimensional similarity relations to construct a user similarity network. Finally, the community structure of the mobile network is detected by the use of the bounded non-negative matrix factorization. Experimental results on Foursquare and telecom data sets show that the method can effectively detect the community structure in the mobile network.